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A finite element/neural network framework for modeling suspensions of non-spherical particles. Concepts and medical applications. (English) Zbl 1468.76041

Summary: An accurate prediction of the translational and rotational motion of particles suspended in a fluid is only possible if a complete set of correlations for the force coefficients of fluid-particle interaction is known. The present study is thus devoted to the derivation and validation of a new framework to determine the drag, lift, rotational and pitching torque coefficients for different non-spherical particles in a fluid flow. The motivation for the study arises from medical applications, where particles may have an arbitrary and complex shape. Here, it is usually not possible to derive accurate analytical models for predicting the different hydrodynamic forces. The presented model is designed to be applicable to a broad range of shapes. Another important feature of the suspensions occurring in medical and biological applications is the high number of particles. The modelling approach we propose can be efficiently used for simulations of solid-liquid suspensions with numerous particles. Based on resolved numerical simulations of prototypical particles we generate data to train a neural network which allows us to quickly estimate the hydrodynamic forces experienced by a specific particle immersed in a fluid.

MSC:

76M10 Finite element methods applied to problems in fluid mechanics
76T20 Suspensions
76M99 Basic methods in fluid mechanics
76Z05 Physiological flows
92C35 Physiological flow

Software:

PyTorch; GASCOIGNE
PDFBibTeX XMLCite
Full Text: DOI arXiv

References:

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